Quantum Machine Learning That Works With Infinite Dimensions Developed

Infinity now works with quantum machine learning algorithm. This could make the new quantum machine operate faster than the classical algorithm and could be used in many other applications.

The study was published in the recent issue of Physical Review Letters. It was led by Hoi-Kwan Lau and other colleagues. The team of physicists developed the quantum machine learning algorithm that works with continuous variables that are an infinite number of possible values on a closed interval.

George Siopsis, a co-author of the study from the University of Tennessee, told Phys.org that their work demonstrates the ability to take advantage of photonics to perform machine learning tasks on a quantum computer that could far exceed the speed of any conventional computer. He further said that quantum machine learning also offers potential advantages such as lower energy requirements owing to the ability to store more information per qubit and a very low cost per qubit compared to other technologies.

So, how did the scientists come up with working with continuous variables? The team replaces the logic gates, which are used for discrete-variable states with physical states that work for continuous-variable states. This is considered the basic building blocks of the algorithm. Then, the researchers developed new methods that empower the quantum machine learning problems known as subroutines that are represented by matrices and vectors.

The development of the new algorithm is expected to be implemented using the currently available technology. This can be done using the optical system, spin systems or trapped atoms.

Quantum machine learning is an interdisciplinary research area that involves quantum physics and data mining. It merges methods from quantum information science and machine learning. Its algorithms can utilize the advantages of quantum computation to enhance the classical methods of machine learning. This also refers to quantum algorithms that solve the tasks in machine learning resulting for the enhancement of a classical machine learning method.